Synthetic datasets of high quality and realism can be used to accelerate methodological advancements in medicine [1]}, [2]}. While there are methods for generating medical data for electronic health records [1]}, [2]}, the study of medical image synthesis is more difficult as medical images are high-dimensional.
With the growth of generative adversarial networks (GAN) [5]}, high-dimensional image generation became possible. Particularly, conditional GAN [6]} can generate images conditional on a given mode, e.g. constructing images based on their labels to synthesize labeled datasets. In the field of medical images synthesis,  [7]} proposed a progressive growing conditional GAN to generate lung cancer images and concluded that synthetic images can assist deep convolution neural network training.  [8]} recently used conditional GAN-generated pictures of aberrant cells in cervical cell classification to address the class imbalance issue.
In addition, a comprehensive survey about the role of GAN-based argumentation in medical images can be found in [9]}.
